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21-20. Rapid, robust quantification of earthquake uncertainties to unlock advanced monitoring, forecasting, and research


Closing Date: November 1, 2022

This Research Opportunity will be filled depending on the availability of funds. All application materials must be submitted through USAJobs by 11:59 pm, US Eastern Standard Time, on the closing date.

Please communicate with individual Research Advisor(s) on the right to discuss project ideas and answer specific questions about the Research Opportunity.

How to Apply


Earthquake monitoring forms the foundation of earthquake forecasting and research.  Although overall earthquake monitoring capabilities have become increasingly sophisticated in the past decades, methods of uncertainty quantification, key to earthquake forecasting and other applications, have remained primitive.  Currently, uncertainties in fundamental earthquake source parameters, such as location and magnitude, are not standardized and are usually derived only from misfit within a particular assumed model (also known as “model errors”) or from ad hoc, outdated approximations. Characterizations like these ignore significant sources of uncertainty and bias (such as from discrepancies between different magnitude types or errors in the velocity model) and often dramatically underestimate the total uncertainty.  Current shortcomings in uncertainty quantification significantly impair impactful products that build upon earthquake monitoring and the associated catalogs, and perhaps less obviously, earthquake monitoring itself. 

The current paradigm of earthquake monitoring relies heavily on expert human judgement, both for identification and timing of seismic phases, and just as critically, for deciding when solution quality is sufficiently high to warrant public release.  Although this approach has been successful within its scope, it does not readily scale to rapidly monitor earthquakes to the lower magnitudes that are recorded with modern, increasingly dense, instrumentation, thus necessitating a new approach. Recent developments in machine learning are poised to revolutionize aspects of seismic processing, such as seismic phase picking; but a key piece of an automated system remains underdeveloped: how do we determine overall solution quality?  Methods for rapid release of automatic earthquake locations and magnitudes have been implemented on local scales within dense networks. Examples include automatic solutions published within minutes by several ANSS regional seismic networks, and alerts distributed within seconds from the ShakeAlert Earthquake Early Warning (EEW) system.  At regional and global scales uncertainty analysis becomes more difficult because of lower signal-to-noise ratios and sparse and uneven station coverage. Simple metrics such as numbers of phases recorded and associated azimuthal gaps provide rudimentary checks sufficient for dense local networks, but they are inadequate in more complex cases encountered in regional and global earthquake monitoring.  A new approach to uncertainty quantification is needed to guide release of automatic solutions and to measure improvements in monitoring performance over time.

Meanwhile, earthquake forecasting relies critically on earthquake monitoring, including Operational Aftershock Forecasting (OAF) at short timescales (days to months) and the National Seismic Hazard Model (NSHM) on longer timescales (years to decades). Both approaches rely on the statistics of the earthquake catalog, particularly with respect to earthquake magnitudes. Furthermore, large biases may exist between earthquake magnitudes calculated by different techniques. Lacking robust, earthquake-specific measures of uncertainty, earthquake forecasting methods must revert to broad brush approximations, resulting in decreased resolution and accuracy.  Realistic uncertainties in real-time source parameters, especially for magnitudes, will pave the way for future advances in earthquake forecasting, including potentially bridging the gap between OAF and NSHM forecasting timescales in comprehensive operational earthquake forecasting.

The focus of this Mendenhall Research Opportunity is to create a new framework for estimating uncertainties in earthquake source parameters.  Addressing this problem requires innovative research into new approaches to quantifying location and magnitude uncertainties.

We invite proposals from candidates with research experience in observational seismology, computational seismology, geophysics, and/or computer science.  Research should be targeted to approaches that can eventually be integrated into processing workflows at the National Earthquake Information Center (NEIC) and other monitoring networks.  This requires that the analysis can be performed rapidly, so that it can be implemented in real-time earthquake processing to assess automatic solutions and feed into rapid products such as OAF.  The approach should also be robust, such that it can accurately characterize uncertainties across scales (local to global) with widely varying network coverage.  The research will start with a review of existing techniques for source parameter uncertainty estimation used by the Advance National Seismic System (ANSS) and by EEW, then will investigate potential unifying approaches possibly incorporating Bayesian, empirical, or bootstrap techniques. Novel combinations of these approaches should be considered, perhaps through the use of machine-learning tools leveraging large datasets.

Testing the accuracy of these tools by comparing the performance to standard methodologies is essential to ensuring their applicability to real-time operations. Candidates will be encouraged to propose not only methods for how to improve real-time automated source-parameter estimation but also to quantify the performance of different methods including current operational methods in order to produce rigorous criteria for accepting the use of new automated methods.

In addition to developing tools to facilitate real-time monitoring and forecasting, we encourage the application of these tools to advance our understanding of seismotectonics and the earthquake process. Improved uncertainty characterization may have fundamental implications for interpretations of seismic catalogs. As just one example with important scientific consequences, the newly developed tools could be used to assess whether or not earthquake locations near a given fault can be explained by activity on a single fault (that is, their scatter can be explained fully by location uncertainty), or if the scatter instead implies activation of multiple faults.

Interested applicants are strongly encouraged to contact the Research Advisor(s) early in the application process to discuss project ideas.

Proposed Duty Station(s): Golden, Colorado; Moffett Field, California

Areas of PhD: Geophysics, seismology, or related fields (candidates holding a Ph.D. in other disciplines, but with extensive knowledge and skills relevant to the Research Opportunity may be considered).

Qualifications: Applicants must meet one of the following qualifications:  Research Geophysicist, Research Civil Engineer, Research Computer Scientist, or Research Geologist.

(This type of research is performed by those who have backgrounds for the occupations stated above.  However, other titles may be applicable depending on the applicant's background, education, and research proposal. The final classification of the position will be made by the Human Resources specialist.)

Human Resources Office Contact:  Danial Anthon, 303-236-9197,

Apply Here